Today's software development is distributed and involves continuous changes for new features and yet, their development cycle has to be fast and agile. An important component of enabling this agility is selecting the right reviewers for every code-change - the smallest unit of the development cycle. Modern tool-based code review is proven to be an effective way to achieve appropriate code review of software changes. However, the selection of reviewers in these code review systems is at best manual. As software and teams scale, this poses the challenge of selecting the right reviewers, which in turn determines software quality over time. While previous work has suggested automatic approaches to code reviewer recommendations, it has been limited to retrospective analysis. We not only deploy a reviewer suggestions algorithm - WhoDo - and evaluate its effect but also incorporate load balancing as part of it to address one of its major shortcomings: of recommending experienced developers very frequently. We evaluate the effect of this hybrid recommendation + load balancing system on five repositories within Microsoft. Our results are based around various aspects of a commit and how code review affects that. We attempt to quantitatively answer questions which are supposed to play a vital role in effective code review through our data and substantiate it through qualitative feedback of partner repositories.

Emails continue to remain the most important and widely used mode of online communication despite having its origins in the middle of last century and being threatened by a variety of online communication innovations. While several studies have predicted the continuous growth of volume of email communication, there is little innovation on improving the search in emails, an imperative part of the user experience. In this work, we present a lightweight email application codenamed InLook, that intends to provide a productive search experience.

Due to the recent emergence of massive open online courses (MOOCs), students and teachers are gaining unprecedented access to high-quality educational content. However, many questions remain on how best to utilize that content in a classroom environment. In this small-scale, exploratory study, we compared two ways of using a recorded video lecture. In the online learning condition, students viewed the video on a personal computer, and also viewed a follow-up tutorial (a quiz review) on the computer. In the blended learning condition, students viewed the video as a group in a classroom, and received the follow-up tutorial from a live lecturer. We randomly assigned 102 students to these conditions, and assessed learning outcomes via a series of quizzes. While we saw significant learning gains after each session conducted, we did not observe any significant differences between the online and blended learning groups. We discuss these findings as well as areas for future work.

In large software development projects, when a programmer is assigned a bug to fix, she typically spends a lot of time searching (in an ad-hoc manner) for instances from the past where similar bugs have been debugged, analyzed and resolved. Systematic search tools that allow the programmer to express the context of the current bug, and search through diverse data repositories associated with large projects can greatly improve the productivity of debugging This paper presents the design, implementation and experience from such a search tool called DebugAdvisor.